Object Detection. Following [1]}, we adopt a Mask-RCNN [2]} framework to evaluate the pre-trained models on COCO object detection. All models are trained with a 3\(\times \)  schedule (36 epochs). We utilize an AdamW [3]} optimizer with a learning rate of 6e-5 for supervised model and a learning rate of 8e-5 for MIM model, a weight decay of 0.05 and a batch size of 32 for both models. Following [4]}, [5]}, we employ a large jittering augmentation (1024 \(\times \)  1024 resolution, scale range [0.1, 2.0]). The window size is set to 14 for both models and drop path rate is set to 0.3 for supervised model and 0.1 for MIM model. AP\(^{box}\)  and AP\(^{mask}\)  are reported for comparison.
